120 citations · 132 across the 14 of their papers we have counts for
14 papers
A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges
Chengcheng Sun, Yajie Song, Cheng Zhai +6
Graph Neural Networks (GNNs) have emerged as the leading paradigm for link prediction, enabling the inference of missing connections and the anticipation of potential future links.…
Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey
Chengcheng Sun, Jiayun Tian, Cheng Zhai +5
Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data. However, there remains a l…
Attention-based graph neural networks: a survey
Chengcheng Sun, Chenhao Li, Xiang Lin +4
Graph neural networks (GNNs) aim to learn well-trained representations in a lower-dimension space for downstream tasks while preserving the topological structures. In recent years,…
Scalable Fair Influence Blocking Maximization via Approximately Monotonic Submodular Optimization
Qiangpeng Fang, Jilong Shi, Xiaobin Rui +2
Influence Blocking Maximization (IBM) aims to select a positive seed set to suppress the spread of negative influence. However, existing IBM methods focus solely on maximizing bloc…
GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
Zhixiao Wang, Chaofan Zhu, Qihan Feng +3
Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations.…
GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
Chaofan Zhu, Xiaobing Rui, Zhixiao Wang
Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations.…
Time-Critical Adversarial Influence Blocking Maximization
Jilong Shi, Qiangpeng Fang, Xiaobin Rui +2
Adversarial Influence Blocking Maximization (AIBM) aims to select a set of positive seed nodes that propagate synchronously with the known negative seed nodes to counteract their n…
Efficient Approximation Algorithms for Fair Influence Maximization under Maximin Constraint
Xiaobin Rui, Qiangpeng Fang, Chen Peng +3
Fair Influence Maximization (FIM) seeks to mitigate disparities in influence across different groups and has recently garnered increasing attention. A widely adopted notion of fair…
Boosting Commit Classification with Contrastive Learning
Jiajun Tong, Zhixiao Wang, Xiaobin Rui
Commit Classification (CC) is an important task in software maintenance, which helps software developers classify code changes into different types according to their nature and pu…
Incorprating Prompt tuning for Commit classification with prior Knowledge
Jiajun Tong, Xiaobin Rui
Commit Classification(CC) is an important task in software maintenance since it helps software developers classify code changes into different types according to their nature and p…
Scalable Fair Influence Maximization
Xiaobin Rui, Zhixiao Wang, Jiayu Zhao +2
Given a graph , a community structure , and a budget , the fair influence maximization problem aims to select a seed set () that maximizes the inf…
Scalable Adversarial Attack Algorithms on Influence Maximization
Lichao Sun, Xiaobin Rui, Wei Chen
In this paper, we study the adversarial attacks on influence maximization under dynamic influence propagation models in social networks. In particular, given a known seed set S, th…
A topic-aware graph neural network model for knowledge base updating
Jiajun Tong, Zhixiao Wang, Xiaobin Rui
The open domain knowledge base is very important. It is usually extracted from encyclopedia websites and is widely used in knowledge retrieval systems, question answering systems,…
A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set
Jiajun Tong, Zhixiao Wang, Xiaobin Rui
Text classification plays an important role in many practical applications. In the real world, there are extremely small datasets. Most existing methods adopt pre-trained neural ne…